Biologically motivated computational modeling of chloroform cytolethality and regenerative cellular proliferation

Evaluating the impact of anatomical and physiological variability on human equivalent doses using PBPK models

Addressing human anatomical and physiological variability is a crucial component of human health risk assessment of chemicals. Experts have recommended probabilistic chemical risk assessment paradigms in which distributional adjustment factors are used to account for various sources of uncertainty and variability, including variability in the pharmacokinetic behavior of a given substance in different humans. In practice, convenient assumptions about the distribution forms of adjustment factors and human equivalent doses (HEDs) are often used. Parameters such as tissue volumes and blood flows are likewise often assumed to be lognormally or normally distributed without evaluating empirical data for consistency with these forms. In this work, we performed dosimetric extrapolations using physiologically based pharmacokinetic (PBPK) models for dichloromethane (DCM) and chloroform that incorporate uncertainty and variability to determine if the HEDs associated with such extrapolations are approximately lognormal and how they depend on the underlying distribution shapes chosen to represent model parameters. We accounted for uncertainty and variability in PBPK model parameters by randomly drawing their values from a variety of distribution types. We then performed reverse dosimetry to calculate HEDs based on animal points of departure for each set of sampled parameters. Corresponding samples of HEDs were tested to determine the impact of input parameter distributions on their central tendencies, extreme percentiles, and degree of conformance to lognormality. This work demonstrates that the measurable attributes of human variability should be considered more carefully and that generalized assumptions about parameter distribution shapes may lead to inaccurate estimates of extreme percentiles of HEDs.

Effect of surface treatment of ethylene vinyl acetate on the delamination of custom-fitted mouthguards

BACKGROUND/AIM

Contamination of ethylene vinyl acetate (EVA) during mouthguard fabrication can cause delamination. The study evaluated the effects of different EVA surface treatments on the contact angle, laminate bond strength, and elongation capacity.

MATERIALS AND METHODS

Specimens of two bonded EVA plates were prepared (n = 30). The Shore A hardness of standardized EVA plate specimens was measured before and after thermo-plasticization. The EVA plates were randomly allocated to one of five different surface treatment groups: no treatment (control); isopropyl alcohol, 100%; chloroform, 99.8%; self-cure acrylic resin monomer (methacrylate, ethylene glycol dimethacrylate, and chemical initiator-amine type); and ethyl alcohol, 70%. The maximum breaking force and elongation at the site of fracture were recorded using a universal testing machine. The contact angle surface was measured using ImageJ software. Scanning electron microscopy of the EVA surface was performed. The laminate bond strength was obtained by dividing the maximum breaking force by the bonding area between the two EVA plates. The laminate bond strength and maximum elongation data were analyzed by one-way ANOVA, followed by the Tukey's and the Dunnet test. The failure mode data was analyzed using the chi-square test (α = .05).

RESULTS

EVA surface treatment significantly influenced the laminate bond strength and maximum elongation (p < .001). The control group had a higher contact angle and significantly lower laminate bond strength and maximum elongation than the other groups (p < .001). The acrylic resin monomer and chloroform-treated specimens had similar laminate bond strength and maximum elongation. The acrylic resin monomer group had a significantly lower contact angle (p < .001).

CONCLUSIONS

All treatments had a significantly higher laminate bond strength and maximum elongation than the control group. The acrylic resin monomer and chloroform groups had a significantly higher laminate bond strength and maximum elongation and the acrylic resin monomer group had a lower contact angle than the other groups. The chloroform should be avoided due its hazardous effects.

Monte-Carlo and multi-exposure assessment for the derivation of criteria for disinfection byproducts and volatile organic compounds in drinking water: Allocation factors and liter-equivalents per day

The probability distributions of total potential doses of disinfection byproducts and volatile organic compounds via ingestion, inhalation, and dermal exposure were estimated with Monte Carlo simulations, after conducting physiologically based pharmacokinetic model simulations to takes into account the differences in availability between the three exposures. If the criterion that the 95th percentile estimate equals the TDI (tolerable daily intake) is regarded as protecting the majority of a population, the drinking water criteria would be 140 (trichloromethane), 66 (bromodichloromethane), 157 (dibromochloromethane), 203 (tribromomethane), 140 (dichloroacetic acid), 78 (trichloroacetic acid), 6.55 (trichloroethylene, TCE), and 22 μg/L (perchloroethylene). The TCE criterion was lower than the Japanese Drinking Water Quality Standard (10 μg/L). The latter would allow the intake of 20% of the population to exceed the TDI. Indirect inhalation via evaporation from water, especially in bathrooms, was the major route of exposure to compounds other than haloacetic acids (HAAs) and accounted for 1.2-9 liter-equivalents/day for the median-exposure subpopulation. The ingestion of food was a major indirect route of exposure to HAAs. Contributions of direct water intake were not very different for trihalomethanes (30-45% of TDIs) and HAAs (45-52% of TDIs).

Developing a Physiologically-Based Pharmacokinetic Model Knowledgebase in Support of Provisional Model Construction

Developing physiologically-based pharmacokinetic (PBPK) models for chemicals can be resource-intensive, as neither chemical-specific parameters nor in vivo pharmacokinetic data are easily available for model construction. Previously developed, well-parameterized, and thoroughly-vetted models can be a great resource for the construction of models pertaining to new chemicals. A PBPK knowledgebase was compiled and developed from existing PBPK-related articles and used to develop new models. From 2,039 PBPK-related articles published between 1977 and 2013, 307 unique chemicals were identified for use as the basis of our knowledgebase. Keywords related to species, gender, developmental stages, and organs were analyzed from the articles within the PBPK knowledgebase. A correlation matrix of the 307 chemicals in the PBPK knowledgebase was calculated based on pharmacokinetic-relevant molecular descriptors. Chemicals in the PBPK knowledgebase were ranked based on their correlation toward ethylbenzene and gefitinib. Next, multiple chemicals were selected to represent exact matches, close analogues, or non-analogues of the target case study chemicals. Parameters, equations, or experimental data relevant to existing models for these chemicals and their analogues were used to construct new models, and model predictions were compared to observed values. This compiled knowledgebase provides a chemical structure-based approach for identifying PBPK models relevant to other chemical entities. Using suitable correlation metrics, we demonstrated that models of chemical analogues in the PBPK knowledgebase can guide the construction of PBPK models for other chemicals.

Approaches for characterizing threshold dose-response relationships for DNA-damage pathways involved in carcinogenicity in vivo and micronuclei formation in vitro

Assessing the shape of dose-response curves for DNA-damage in cellular systems and for the consequences of DNA damage in intact animals remains a controversial topic. This overview looks at aspects of the pharmacokinetics (PK) and pharmacodynamics (PD) of cellular DNA-damage/repair and their role in defining the shape of dose-response curves using an in vivo example with formaldehyde and in vitro examples for micronuclei (MN) formation with several test compounds. Formaldehyde is both strongly mutagenic and an endogenous metabolite in cells. With increasing inhaled concentrations, there were transitions in gene changes, from activation of selective stress pathway genes at low concentrations, to activation of pathways for cell-cycle control, p53-DNA damage, and stem cell niche pathways at higher exposures. These gene expression changes were more consistent with dose-dependent transitions in the PD responses to formaldehyde in epithelial cells in the intact rat rather than the low-dose linear extrapolation methods currently used for carcinogens. However, more complete PD explanations of non-linear dose response for creation of fixed damage in cells require detailed examination of cellular responses in vitro using measures of DNA damage and repair that are not easily accessible in the intact animal. In the second section of the article, we illustrate an approach from our laboratory that develops fit-for-purpose, in vitro assays and evaluates the PD of DNA damage and repair through studies using prototypical DNA-damaging agents. Examination of a broad range of responses in these cells showed that transcriptional upregulation of cell cycle control and DNA repair pathways only occurred at doses higher than those causing overt damage fixed damage-measured as MN formation. Lower levels of damage appear to be handled by post-translational repair process using pre-existing proteins. In depth evaluation of the PD properties of one such post-translational process (formation of DNA repair centers; DRCs) has indicated that the formation of DRCs and their ability to complete repair before replication are consistent with threshold behaviours for mutagenesis and, by extension, with chemical carcinogenesis.

A systematic review of Bisphenol A "low dose" studies in the context of human exposure: a case for establishing standards for reporting "low-dose" effects of chemicals

Human exposure to the chemical Bisphenol A is almost ubiquitous in surveyed industrialized societies. Structural features similar to estrogen confer the ability of Bisphenol A (BPA) to bind estrogen receptors, giving BPA membership in the group of environmental pollutants called endocrine disruptors. References by scientists, the media, political entities, and non-governmental organizations to many toxicity studies as "low dose" has led to the belief that exposure levels in these studies are similar to humans, implying that BPA is toxic to humans at current exposures. Through systematic, objective comparison of our current, and a previous compilation of the "low-dose" literature to multiple estimates of human external and internal exposure levels, we found that the "low-dose" moniker describes exposures covering 8-12 orders of magnitude, the majority (91-99% of exposures) being greater than the upper bound of human exposure in the general infant, child and adult U.S. Population. "low dose" is therefore a descriptor without specific meaning regarding human exposure. Where human exposure data are available, for BPA and other environmental chemicals, reference to toxicity study exposures by direct comparison to human exposure would be more informative, more objective, and less susceptible to misunderstanding.

Application of an updated physiologically based pharmacokinetic model for chloroform to evaluate CYP2E1-mediated renal toxicity in rats and mice

Physiologically based pharmacokinetic (PBPK) models are tools for interpreting toxicological data and extrapolating observations across species and route of exposure. Chloroform (CHCl(3)) is a chemical for which there are PBPK models available in different species and multiple sites of toxicity. Because chloroform induces toxic effects in the liver and kidneys via production of reactive metabolites, proper characterization of metabolism in these tissues is essential for risk assessment. Although hepatic metabolism of chloroform is adequately described by these models, there is higher uncertainty for renal metabolism due to a lack of species-specific data and direct measurements of renal metabolism. Furthermore, models typically fail to account for regional differences in metabolic capacity within the kidney. Mischaracterization of renal metabolism may have a negligible effect on systemic chloroform levels, but it is anticipated to have a significant impact on the estimated site-specific production of reactive metabolites. In this article, rate parameters for chloroform metabolism in the kidney are revised for rats, mice, and humans. New in vitro data were collected in mice and humans for this purpose and are presented here. The revised PBPK model is used to interpret data of chloroform-induced kidney toxicity in rats and mice exposed via inhalation and drinking water. Benchmark dose (BMD) modeling is used to characterize the dose-response relationship of kidney toxicity markers as a function of PBPK-derived internal kidney dose. Applying the PBPK model, it was also possible to characterize the dose response for a recent data set of rats exposed via multiple routes simultaneously. Consistent BMD modeling results were observed regardless of species or route of exposure.

Computational modeling of signaling pathways mediating cell cycle checkpoint control and apoptotic responses to ionizing radiation-induced DNA damage

The shape of dose response of ionizing radiation (IR) induced cancer at low dose region, either linear non-threshold or J-shaped, has been a debate for a long time. This dose response relationship can be influenced by built-in capabilities of cells that minimize the fixation of IR-mediated DNA damage as pro-carcinogenic mutations. Key capabilities include sensing of damage, activation of cell cycle checkpoint arrests that provide time needed for repair of the damage as well as apoptosis. Here we describe computational modeling of the signaling pathways that link sensing of DNA damage and checkpoint arrest activation/apoptosis to investigate how these molecular-level interactions influence the dose response relationship for IR induced cancer. The model provides qualitatively accurate descriptions of the IR-mediated activation of cell cycle checkpoints and the apoptotic pathway, and of time-course activities and dose response of relevant regulatory proteins (e.g. p53 and p21). Linking to a two-stage clonal growth cancer model, the model described here successfully captured a monotonically increasing to a J-shaped dose response curve and identified one potential mechanism leading to the J-shape: the cell cycle checkpoint arrest time saturates with the increase of the dose.

Development of a quantitative model of pregnane X receptor (PXR) mediated xenobiotic metabolizing enzyme induction

The pregnane X receptor plays an integral role in the regulation of hepatic metabolism. It has been shown to regulate CYP3A4, which is the most abundant cytochrome P450 in the human liver. With its large and flexible ligand-binding domain, PXR can be activated by an enormous range of relatively small, hydrophobic, exogenous compounds. Upon activation, PXR partners with the retinoid X receptor (RXR) to form a heterodimer. The newly formed heterodimer binds to an appropriate DNA response element, causing increased transcription. This leads to an induction in the level of CYP3A4. These mechanistic steps are included into a biologically-based mathematical model. The quantitative model predicts fold level inductions of CYP3A4 mRNA and protein in response to PXR activation. Model parameter values have been taken from literature when appropriate. Unknown parameter values are estimated by optimizing the model results to published in vivo and in vitro data sets. A sensitivity analysis is performed to evaluate the model structure and identify future data needs which would be critical to revising the model.

Computational systems biology and dose-response modeling in relation to new directions in toxicity testing

The new paradigm envisioned for toxicity testing in the 21st century advocates shifting from the current animal-based testing process to a combination of in vitro cell-based studies, high-throughput techniques, and in silico modeling. A strategic component of the vision is the adoption of the systems biology approach to acquire, analyze, and interpret toxicity pathway data. As key toxicity pathways are identified and their wiring details elucidated using traditional and high-throughput techniques, there is a pressing need to understand their qualitative and quantitative behaviors in response to perturbation by both physiological signals and exogenous stressors. The complexity of these molecular networks makes the task of understanding cellular responses merely by human intuition challenging, if not impossible. This process can be aided by mathematical modeling and computer simulation of the networks and their dynamic behaviors. A number of theoretical frameworks were developed in the last century for understanding dynamical systems in science and engineering disciplines. These frameworks, which include metabolic control analysis, biochemical systems theory, nonlinear dynamics, and control theory, can greatly facilitate the process of organizing, analyzing, and understanding toxicity pathways. Such analysis will require a comprehensive examination of the dynamic properties of "network motifs"--the basic building blocks of molecular circuits. Network motifs like feedback and feedforward loops appear repeatedly in various molecular circuits across cell types and enable vital cellular functions like homeostasis, all-or-none response, memory, and biological rhythm. These functional motifs and associated qualitative and quantitative properties are the predominant source of nonlinearities observed in cellular dose response data. Complex response behaviors can arise from toxicity pathways built upon combinations of network motifs. While the field of computational cell biology has advanced rapidly with increasing availability of new data and powerful simulation techniques, a quantitative orientation is still lacking in life sciences education to make efficient use of these new tools to implement the new toxicity testing paradigm. A revamped undergraduate curriculum in the biological sciences including compulsory courses in mathematics and analysis of dynamical systems is required to address this gap. In parallel, dissemination of computational systems biology techniques and other analytical tools among practicing toxicologists and risk assessment professionals will help accelerate implementation of the new toxicity testing vision.

Development of a quantitative model incorporating key events in a hepatotoxic mode of action to predict tumor incidence

Biologically based dose-response (BBDR) modeling of environmental pollutants can be utilized to inform the mode of action (MOA) by which compounds elicit adverse health effects. Chemicals that produce tumors are typically labeled as either genotoxic or nongenotoxic. Though both the genotoxic and the nongenotoxic MOA may be operative as a function of dose, it is important to note that the label informs but does not define a MOA. One commonly proposed MOA for nongenotoxic carcinogens is characterized by the key events cytotoxicity and regenerative proliferation. The increased division rate associated with such proliferation can cause an increase in the probability of mutations, which may result in tumor formation. We included these steps in a generalized computational pharmacodynamic (PD) model incorporating cytotoxicity as a MOA for three carcinogens (chloroform, CHCl(3); carbon tetrachloride, CCL(4); and N,N-dimethylformamide, DMF). For each compound, the BBDR model is composed of a chemical-specific physiologically based pharmacokinetic model linked to a PD model of cytotoxicity and cellular proliferation. The rate of proliferation is then linked to a clonal growth model to predict tumor incidences. Comparisons of the BBDR simulations and parameterizations across chemicals suggested that significant variation among the models for the three chemicals arises in a few parameters expected to be chemical specific (such as metabolism and cellular injury rate constants). Optimization of model parameters to tumor data for CCL(4) and DMF resulted in similar estimates for all parameters related to cytotoxicity and tumor incidences. However, optimization of the CHCl(3) data resulted in a higher estimate for one parameter (BD) related to death of initiated cells. This implies that additional steps beyond cytotoxicity leading to induced cellular proliferation can be quantitatively different among chemicals that share cytotoxicity as a hypothesized carcinogenic MOA.

Mode of action considerations in the quantitative assessment of tumour responses in the liver

Chemical carcinogenesis is a complex, multi-stage process and the relationship between dose and tumour formation is an important consideration in the risk assessment of chemicals. Extrapolation from empirical dose-response relationships obtained in experimental studies has been criticized, as it fails to take into account information on mode of action. Strategies for incorporating mode of action information into the risk assessment of chemical carcinogens are described, with a focus on hepatic cancer. Either toxicokinetic or toxicodynamic processes can be addressed. Whilst the former have been the focus of more attention to date, for example by using physiologically based modelling, there is increasing interest in the development of mode of action-based toxicodynamic models. These have the advantage that they do not require extreme assumptions, and may be amenable to paramaterization using human data. This is rarely if ever possible when using conventional dose-tumour response relationships. The approaches discussed are illustrated using chloroform as a case study. This compound is converted to a cytotoxic metabolite, phosgene, by CYP2E1 in liver and/or kidney. Cytotoxicity results in proliferative regeneration, with increased probability of tumour formation. Both physiologically based toxicokinetic and toxicodynamic models have been developed, and it is possible to use probabilistic approaches incorporating, for example, data on the distribution of hepatic CYP2E1 levels. Mode of action can provide an invaluable link between observable, experimental data, on both toxicokinetics and toxicodynamics, and chemical-specific risk assessment, based on physiological approaches.

Applying Mode-of-Action and Pharmacokinetic Considerations in Contemporary Cancer Risk Assessments: An Example with Trichloroethylene

The guidelines for carcinogen risk assessment recently proposed by the U.S. Environmental Protection Agency (U.S. EPA) provide an increased opportunity for the consideration of pharmacokinetic and mechanistic data in the risk assessment process. However, the greater flexibility of the new guidelines can also make their actual implementation for a particular chemical highly problematic. To illuminate the process of performing a cancer risk assessment under the new guidelines, the rationale for a state-of-the-science risk assessment for trichloroethylene (TCE) is presented. For TCE, there is evidence of increased cell proliferation due to receptor interaction or cytotoxicity in every instance in which tumors are observed, and most tumors represent an increase in the incidence of a commonly observed, species-specific lesion. A physiologically based pharmacokinetic (PBPK) model was applied to estimate target tissue doses for the three principal animal tumors associated with TCE exposure: liver, lung, and kidney. The lowest points of departure (lower bound estimates of the exposure associated with 10% tumor incidence) for lifetime human exposure to TCE were obtained for mouse liver tumors, assuming a mode of action primarily involving the mitogenicity of the metabolite trichloroacetic acid (TCA). The associated linear unit risk estimates for mouse liver tumors are 1.5 x 10(-6) for lifetime exposure to 1 microg TCE per cubic meter in air and 0.4 x 10(-6) for lifetime exposure to 1 microg TCE per liter in drinking water. However, these risk estimates ignore the evidence that the human is likely to be much less responsive than the mouse to the carcinogenic effects of TCA in the liver and that the carcinogenic effects of TCE are unlikely to occur at low environmental exposures. Based on consideration of the most plausible carcinogenic modes of action of TCE, a margin-of-exposure (MOE) approach would appear to be more appropriate. Applying an MOE of 1000, environmental exposures below 66 microg TCE per cubic meter in air and 265 microg TCE per liter in drinking water are considered unlikely to present a carcinogenic hazard to human health.

Approaches for applications of physiologically based pharmacokinetic models in risk assessment

Physiologically based pharmacokinetic (PBPK) models are particularly useful for simulating exposures to environmental toxicants for which, unlike pharmaceuticals, there is often little or no human data available to estimate the internal dose of a putative toxic moiety in a target tissue or an appropriate surrogate. This article reviews the current state of knowledge and approaches for application of PBPK models in the process of deriving reference dose, reference concentration, and cancer risk estimates. Examples drawn from previous U.S. Environmental Protection Agency (EPA) risk assessments and human health risk assessments in peer-reviewed literature illustrate the ways and means of using PBPK models to quantify the pharmacokinetic component of the interspecies and intraspecies uncertainty factors as well as to conduct route to route, high dose to low dose and duration extrapolations. The choice of the appropriate dose metric is key to the use of the PBPK models for the various applications in risk assessment. Issues related to whether uncertainty factors are most appropriately applied before or after derivation of human equivalent dose (or concentration) continue to be explored. Scientific progress in the understanding of life stage and genetic differences in dosimetry and their impacts on variability in susceptibility, as well as ongoing development of analytical methods to characterize uncertainty in PBPK models, will make their use in risk assessment increasingly likely. As such, it is anticipated that when PBPK models are used to express adverse tissue responses in terms of the internal target tissue dose of the toxic moiety rather than the external concentration, the scientific basis of, and confidence in, risk assessments will be enhanced.

On the incorporation of chemical-specific information in risk assessment

This paper describes the evolution of chemical risk assessment from its early dependence on generic default approaches to the current situation in which mechanistic and biokinetic data are routinely incorporated to support a more chemical-specific approach. Two methodologies that have played an important role in this evolution are described: mode-of-action evaluation and physiologically based biokinetic (PBBK) modelling. When used together, these techniques greatly increase the opportunity for the incorporation of biokinetic and mechanistic data in risk assessment. The resulting risk assessment approaches are more appropriately tailored to the specific chemical and are more likely to provide an accurate assessment of the potential hazards associated with human exposures. The appropriate application of PBBK models in risk assessment demands well-formulated statements about the chemical mode of action. It is this requirement for an explicit, mechanistic hypothesis that gives biologically motivated models their power, but at the same time serves as the greatest impediment to the acceptance of a chemical-specific risk assessment approach by regulators. The chief impediment to the regulatory acceptance and application of PBBK models in risk assessment is concern about uncertainties associated with their use. To some extent such concerns can be addressed by the development of generally accepted approaches for model evaluation and quantitative uncertainty analysis. In order to assure the protection of public health while limiting the economic and social consequences of over-regulation, greater dialogue between researchers and regulators is crucially needed to foster an increased use of emerging scientific information and innovative methods in chemical risk assessments.

Bayesian estimation of pharmacokinetic and pharmacodynamic parameters in a mode-of-action-based cancer risk assessment for chloroform

Chloroform is a carcinogen in rodents and its carcinogenicity is secondary to events associated with cytotoxicity and regenerative cell proliferation. In this study, a physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model that links the processes of chloroform metabolism, reparable cell damage, cell death, and regenerative cellular proliferation was developed to support a new cancer dose-response assessment for chloroform. Model parameters were estimated using Markov Chain Monte Carlo (MCMC) analysis in a two-step approach: (1) metabolism parameters for male and female mice and rats were estimated against available closed chamber gas uptake data; and (2) PD parameters for each of the four rodent groups were estimated from hepatic and renal labeling index data following inhalation exposures. Subsequently, the resulting rodent PD parameters together with literature values for human age-dependent physiological and metabolism parameters were used to scale up the rodent model to a human model. The human model was used to predict exposure conditions under which chloroform-mediated cytolethality is expected to occur in liver and kidney of adults and children. Using the human model, inhalation Reference Concentrations (RfCs) and oral Reference Doses (RfDs) were derived using an uncertainty factor of 10. Based on liver and kidney dose metrics, the respective RfCs were 0.9 and 0.09 ppm; and the respective RfDs were 0.4 and 3 mg/kg/day.

Ranking cancer risks of organic hazardous air pollutants in the United States

BACKGROUND

In this study we compared cancer risks from organic hazardous air pollutants (HAPs) based on total personal exposure summed across different microenvironments and exposure pathways.

METHODS

We developed distributions of personal exposure concentrations using field monitoring and modeling data for inhalation and, where relevant, ingestion pathways. We calculated risks for a nonoccupationally exposed and nonsmoking population using U.S. Environmental Protection Agency (EPA) and California Office of Environmental Health and Hazard Assessment (OEHHA) unit risks. We determined the contribution to risk from indoor versus outdoor sources using indoor/outdoor ratios for gaseous compounds and the infiltration factor for particle-bound compounds.

RESULTS

With OEHHA's unit risks, the highest ranking compounds based on the population median are 1,3-butadiene, formaldehyde, benzene, and dioxin, with risks on the order of 10(-4)-10(-5). The highest risk compounds with the U.S. EPA unit risks were dioxin, benzene, formaldehyde, and chloroform, with risks on a similar order of magnitude. Although indoor exposures are responsible for nearly 70% of risk using OEHHA's unit risks, when infiltration is accounted for, inhalation of outdoor sources contributed 50% to total risk, on average. Additionally, 15% of risk resulted from exposures through food, mainly due to dioxin.

CONCLUSIONS

Most of the polycyclic aromatic hydrocarbon, benzene, acetaldehyde, and 1,3-butadiene risk came from outdoor sources, whereas indoor sources were primarily responsible for chloroform, formaldehyde, and naphthalene risks. The infiltration of outdoor pollution into buildings, emissions from indoor sources, and uptake through food are all important to consider in reducing overall personal risk to HAPs.

Use of a physiologically based pharmacokinetic model to identify exposures consistent with human biomonitoring data for chloroform

Biomonitoring data provide evidence of human exposure to environmental chemicals by quantifying the chemical or its metabolite in a biological matrix. To better understand the correlation between biomonitoring data and environmental exposure, physiologically based pharmacokinetic (PBPK) modeling can be of use. The objective of this study was to use a combined PBPK model with an exposure model for showering to estimate the intake concentrations of chloroform based on measured blood and exhaled breath concentrations of chloroform. First, the predictive ability of the combined model was evaluated with three published studies describing exhaled breath and blood concentrations in people exposed to chloroform under controlled showering events. Following that, a plausible exposure regimen was defined combining inhalation, ingestion, and dermal exposures associated with residential use of water containing typical concentrations of chloroform to simulate blood and exhaled breath concentrations of chloroform. Simulation results showed that inhalation and dermal exposure could contribute substantially to total chloroform exposure. Next, sensitivity analysis and Monte Carlo analysis were performed to investigate the sources of variability in model output. The variability in exposure conditions (e.g., shower duration) was shown to contribute more than the variability in pharmacokinetics (e.g., body weight) to the predicted variability in blood and exhaled breath concentrations of chloroform. Lastly, the model was used in a reverse dosimetry approach to estimate distributions of exposure consistent with concentrations of chloroform measured in human blood and exhaled breath.